





import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


_A					:				List[Any]				=			logging.get_logger(__name__)

_A					:				str				=			{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

_A					:				int				=			{
    """vocab_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/vocab.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/vocab.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/vocab.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/vocab.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/merges.txt""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/merges.txt""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/merges.txt""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/merges.txt""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/tokenizer.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/tokenizer.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/tokenizer.json""",
    },
}

_A					:				Optional[Any]				=			{
    """gpt2""": 10_24,
    """gpt2-medium""": 10_24,
    """gpt2-large""": 10_24,
    """gpt2-xl""": 10_24,
    """distilgpt2""": 10_24,
}



class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		Optional[Any]				=	VOCAB_FILES_NAMES
    lowerCamelCase__    :		Any				=	PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase__    :		int				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase__    :		Union[str, Any]				=	["""input_ids""", """attention_mask"""]
    lowerCamelCase__    :		Tuple				=	GPTaTokenizer



    def __init__(       self   ,				A_=None   ,				A_=None   ,				A_=None   ,				A_="<|endoftext|>"   ,				A_="<|endoftext|>"   ,				A_="<|endoftext|>"   ,				A_=False   ,				**A_   ,				):





        '''simple docstring'''

        super().__init__(
            A_   ,				A_   ,				tokenizer_file=A_   ,				unk_token=A_   ,				bos_token=A_   ,				eos_token=A_   ,				add_prefix_space=A_   ,				**A_   ,				)

        SCREAMING_SNAKE_CASE__											=			kwargs.pop('''add_bos_token'''   ,				A_     )

        SCREAMING_SNAKE_CASE__											=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
        if pre_tok_state.get('''add_prefix_space'''   ,				A_     ) != add_prefix_space:
            SCREAMING_SNAKE_CASE__											=			getattr(A_   ,				pre_tok_state.pop('''type'''     )     )
            SCREAMING_SNAKE_CASE__											=			add_prefix_space
            SCREAMING_SNAKE_CASE__											=			pre_tok_class(**A_     )

        SCREAMING_SNAKE_CASE__											=			add_prefix_space



    def  lowercase_					(       self   ,				*A_   ,				**A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			kwargs.get('''is_split_into_words'''   ,				A_     )
        assert self.add_prefix_space or not is_split_into_words, (
            f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
            "to use it with pretokenized inputs."
        )

        return super()._batch_encode_plus(*A_   ,				**A_     )



    def  lowercase_					(       self   ,				*A_   ,				**A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			kwargs.get('''is_split_into_words'''   ,				A_     )

        assert self.add_prefix_space or not is_split_into_words, (
            f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
            "to use it with pretokenized inputs."
        )

        return super()._encode_plus(*A_   ,				**A_     )



    def  lowercase_					(       self   ,				A_   ,				A_ = None     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self._tokenizer.model.save(A_   ,				name=A_     )
        return tuple(A_     )




    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			[]
        for is_user, text in conversation.iter_texts():
            input_ids.extend(self.encode(A_   ,				add_special_tokens=A_     ) + [self.eos_token_id]     )

        if len(A_     ) > self.model_max_length:
            SCREAMING_SNAKE_CASE__											=			input_ids[-self.model_max_length :]
        return input_ids




"""simple docstring"""



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


lowercase_							=							logging.get_logger(__name__)

lowercase_							=							{'vocab_file': 'vocab.txt'}

lowercase_							=							{
    'vocab_file': {
        'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt',
        'YituTech/conv-bert-medium-small': (
            'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'
        ),
        'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt',
    }
}

lowercase_							=							{
    'YituTech/conv-bert-base': 5_12,
    'YituTech/conv-bert-medium-small': 5_12,
    'YituTech/conv-bert-small': 5_12,
}


lowercase_							=							{
    'YituTech/conv-bert-base': {'do_lower_case': True},
    'YituTech/conv-bert-medium-small': {'do_lower_case': True},
    'YituTech/conv-bert-small': {'do_lower_case': True},
}







class    __a						( __snake_case							):
				lowerCamelCase     :       Optional[int]			       =VOCAB_FILES_NAMES
				lowerCamelCase     :       List[str]			       =PRETRAINED_VOCAB_FILES_MAP
				lowerCamelCase     :       str			       =PRETRAINED_INIT_CONFIGURATION
				lowerCamelCase     :       Any			       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCamelCase     :       List[str]			       =ConvBertTokenizer







				def __init__( self      ,	UpperCAmelCase=None      ,	UpperCAmelCase=None      ,	UpperCAmelCase=True      ,	UpperCAmelCase="[UNK]"      ,	UpperCAmelCase="[SEP]"      ,	UpperCAmelCase="[PAD]"      ,	UpperCAmelCase="[CLS]"      ,	UpperCAmelCase="[MASK]"      ,	UpperCAmelCase=True      ,	UpperCAmelCase=None      ,	**UpperCAmelCase      ,	):

					'''simple docstring'''






					super().__init__(
					    UpperCAmelCase      ,	tokenizer_file=UpperCAmelCase      ,	do_lower_case=UpperCAmelCase      ,	unk_token=UpperCAmelCase      ,	sep_token=UpperCAmelCase      ,	pad_token=UpperCAmelCase      ,	cls_token=UpperCAmelCase      ,	mask_token=UpperCAmelCase      ,	tokenize_chinese_chars=UpperCAmelCase      ,	strip_accents=UpperCAmelCase      ,	**UpperCAmelCase      ,	)

					lowerCAmelCase_      							=			json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
					if (
					    normalizer_state.get('''lowercase'''      ,	UpperCAmelCase   ) != do_lower_case
					    or normalizer_state.get('''strip_accents'''      ,	UpperCAmelCase   ) != strip_accents
					    or normalizer_state.get('''handle_chinese_chars'''      ,	UpperCAmelCase   ) != tokenize_chinese_chars
					):
						lowerCAmelCase_      							=			getattr(UpperCAmelCase      ,	normalizer_state.pop('''type'''   )   )
						lowerCAmelCase_      							=			do_lower_case
						lowerCAmelCase_      							=			strip_accents
						lowerCAmelCase_      							=			tokenize_chinese_chars
						lowerCAmelCase_      							=			normalizer_class(**UpperCAmelCase   )

					lowerCAmelCase_      							=			do_lower_case







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase=None   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			[self.cls_token_id] + token_ids_a + [self.sep_token_id]

					if token_ids_a:
						output += token_ids_a + [self.sep_token_id]

					return output







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase = None   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			[self.sep_token_id]
					lowerCAmelCase_      							=			[self.cls_token_id]
					if token_ids_a is None:
						return len(cls + token_ids_a + sep   ) * [0]
					return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase = None   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self._tokenizer.model.save(UpperCAmelCase      ,	name=UpperCAmelCase   )
					return tuple(UpperCAmelCase   )